Editors:
- Over 250 entries covering key concepts and terms in the broad field of machine learning. Entries include in-depth essays and definitions, historical background, key applications, and bibliographies
- Extensive cross-references support efficient, user-friendly searchers for immediate access to useful information
- Serves to open the field to those inquiring into this fast-growing area of research
- Includes supplementary material: sn.pub/extras
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Table of contents (892 entries)
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A
Editors and Affiliations
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School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Claude Sammut
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Faculty of Information Technology, Clayton School of Information Technology, Monash University, Victoria, Australia
Geoffrey I. Webb
About the editors
Claude Sammut is a Professor of Computer Science and Engineering at the University of New South Wales, Australia, and Head of the Artificial Intelligence Research Group. He is the UNSW node Director of the ARC Centre of Excellence for Autonomous Systems and a member of the joint ARC/NH&MRC project on Thinking Systems. He is on the editorial boards of the Journal of Machine Learning Research, the Machine Learning Journal and New Generation Computing, and was the chairman of the 2007 International Conference on Machine Learning.
Geoffrey I. Webb is research professor in the faculty of Information Technology at Monash University, Melbourne, Australia. He has published more than 150 scientific papers and is the author of the data mining software package Magnum Opus. His research areas include strategies for strengthening the Naïve Bayes machine learning technique, K-optimal pattern discovery, and work on Occam’s razor. He is editor-in-chief of Springer’s Data Mining and Knowledge Discovery journal, as well as being on the editorial board of Machine Learning.
Bibliographic Information
Book Title: Encyclopedia of Machine Learning
Editors: Claude Sammut, Geoffrey I. Webb
DOI: https://doi.org/10.1007/978-0-387-30164-8
Publisher: Springer New York, NY
eBook Packages: Computer Science, Reference Module Computer Science and Engineering
Copyright Information: Springer Science+Business Media, LLC 2010
Edition Number: 1
Number of Pages: XXVI, 1031
Number of Illustrations: 95 illustrations in colour
Topics: Artificial Intelligence, Artificial Intelligence, Data Mining and Knowledge Discovery, Pattern Recognition, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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